HW1
D.Smutin
2024-04-03
Homework 1 by Smutin Daniil
Prepare data
data("BCI")
data("BCI.env")
BCI## Abarema.macradenia Vachellia.melanoceras Acalypha.diversifolia
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 1 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 2 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 1 0
## 33 0 0 0
## 34 0 0 1
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 1
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## Acalypha.macrostachya Adelia.triloba Aegiphila.panamensis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 3 0
## 5 0 1 1
## 6 0 0 0
## 7 0 0 1
## 8 0 0 0
## 9 0 5 0
## 10 0 0 1
## 11 0 0 0
## 12 0 1 1
## 13 0 1 1
## 14 0 0 0
## 15 0 2 0
## 16 0 2 0
## 17 0 0 1
## 18 0 1 1
## 19 0 0 1
## 20 0 0 2
## 21 0 0 1
## 22 0 1 0
## 23 0 0 0
## 24 0 2 1
## 25 0 0 1
## 26 0 0 0
## 27 0 1 4
## 28 1 0 1
## 29 0 1 0
## 30 0 14 2
## 31 0 5 0
## 32 0 7 0
## 33 0 3 1
## 34 0 3 0
## 35 0 6 0
## 36 0 1 0
## 37 0 2 0
## 38 0 6 0
## 39 0 9 0
## 40 0 7 0
## 41 0 0 1
## 42 0 0 0
## 43 0 0 1
## 44 0 4 0
## 45 0 0 0
## 46 0 0 0
## 47 0 2 0
## 48 0 1 0
## 49 0 0 0
## 50 0 1 0
## Alchornea.costaricensis Alchornea.latifolia Alibertia.edulis
## 1 2 0 0
## 2 1 0 0
## 3 2 0 0
## 4 18 0 0
## 5 3 0 0
## 6 2 1 0
## 7 0 0 0
## 8 2 0 0
## 9 2 0 0
## 10 2 0 0
## 11 10 0 0
## 12 3 0 0
## 13 1 0 1
## 14 4 0 0
## 15 2 0 0
## 16 2 0 0
## 17 2 0 0
## 18 0 0 0
## 19 1 0 0
## 20 2 0 0
## 21 2 0 0
## 22 4 0 0
## 23 1 0 0
## 24 0 0 0
## 25 2 0 0
## 26 3 0 0
## 27 3 0 0
## 28 2 0 0
## 29 1 0 0
## 30 6 0 0
## 31 4 0 0
## 32 6 0 0
## 33 3 0 0
## 34 5 0 0
## 35 8 0 0
## 36 3 0 0
## 37 4 0 0
## 38 2 0 0
## 39 3 0 0
## 40 3 0 0
## 41 11 0 0
## 42 0 0 0
## 43 3 0 0
## 44 4 0 0
## 45 0 0 0
## 46 0 0 0
## 47 1 0 0
## 48 3 0 0
## 49 6 0 0
## 50 2 0 0
## Allophylus.psilospermus Alseis.blackiana Amaioua.corymbosa
## 1 0 25 0
## 2 0 26 0
## 3 0 18 0
## 4 0 23 0
## 5 1 16 0
## 6 0 14 0
## 7 0 18 0
## 8 0 14 0
## 9 0 16 0
## 10 0 14 0
## 11 0 14 0
## 12 2 19 0
## 13 1 8 0
## 14 0 17 0
## 15 0 15 0
## 16 3 25 0
## 17 2 31 0
## 18 0 7 0
## 19 1 13 0
## 20 0 10 0
## 21 1 12 0
## 22 4 22 0
## 23 0 5 0
## 24 1 14 0
## 25 0 20 0
## 26 0 7 0
## 27 3 17 0
## 28 0 16 0
## 29 0 15 0
## 30 0 36 0
## 31 0 11 0
## 32 0 21 0
## 33 1 24 0
## 34 0 42 0
## 35 0 93 0
## 36 0 8 0
## 37 0 19 0
## 38 1 25 0
## 39 1 38 0
## 40 1 65 0
## 41 0 13 0
## 42 0 13 0
## 43 0 8 0
## 44 0 13 0
## 45 0 10 0
## 46 0 29 2
## 47 1 17 0
## 48 1 12 0
## 49 1 6 1
## 50 1 9 0
## Anacardium.excelsum Andira.inermis Annona.spraguei Apeiba.glabra
## 1 0 0 1 13
## 2 0 0 0 12
## 3 0 0 1 6
## 4 0 0 0 3
## 5 0 1 0 4
## 6 0 1 0 10
## 7 0 0 0 5
## 8 1 0 1 4
## 9 0 1 1 5
## 10 0 0 0 5
## 11 1 1 0 2
## 12 0 0 0 4
## 13 0 0 3 1
## 14 0 1 4 5
## 15 0 0 2 8
## 16 0 0 0 5
## 17 0 0 1 1
## 18 2 0 2 1
## 19 0 2 1 13
## 20 0 0 2 6
## 21 2 0 0 2
## 22 0 1 0 7
## 23 2 1 0 3
## 24 1 2 0 6
## 25 1 0 0 6
## 26 0 2 0 4
## 27 0 0 0 2
## 28 2 1 0 6
## 29 0 1 0 5
## 30 0 2 0 5
## 31 0 0 0 5
## 32 0 0 0 4
## 33 0 2 0 8
## 34 0 1 0 3
## 35 0 0 0 5
## 36 0 0 1 3
## 37 0 0 0 4
## 38 0 0 1 1
## 39 0 0 0 3
## 40 2 0 0 7
## 41 2 1 0 2
## 42 0 1 0 5
## 43 0 2 0 2
## 44 0 0 0 7
## 45 0 0 0 1
## 46 0 0 3 0
## 47 0 0 1 8
## 48 3 1 2 2
## 49 1 2 0 4
## 50 2 1 0 3
## Apeiba.tibourbou Aspidosperma.desmanthum Astrocaryum.standleyanum
## 1 2 0 0
## 2 0 0 2
## 3 1 0 1
## 4 1 1 5
## 5 0 1 6
## 6 0 1 2
## 7 0 0 2
## 8 1 0 0
## 9 0 0 2
## 10 0 1 1
## 11 0 0 3
## 12 0 0 1
## 13 1 0 12
## 14 1 1 5
## 15 0 0 0
## 16 0 0 5
## 17 0 1 1
## 18 1 0 6
## 19 1 0 2
## 20 1 0 0
## 21 0 2 3
## 22 0 0 8
## 23 0 0 17
## 24 0 0 7
## 25 0 3 13
## 26 1 0 4
## 27 0 0 5
## 28 0 2 4
## 29 0 2 1
## 30 1 0 1
## 31 0 0 3
## 32 0 1 4
## 33 1 3 10
## 34 1 3 3
## 35 1 0 3
## 36 0 3 2
## 37 0 1 2
## 38 0 3 1
## 39 0 5 1
## 40 0 3 3
## 41 0 0 3
## 42 1 2 13
## 43 0 3 1
## 44 0 2 6
## 45 0 0 1
## 46 3 1 6
## 47 1 2 5
## 48 0 2 7
## 49 0 1 4
## 50 1 2 4
## Astronium.graveolens Attalea.butyracea Banara.guianensis
## 1 6 0 0
## 2 0 1 0
## 3 1 0 0
## 4 3 0 0
## 5 0 0 0
## 6 1 1 0
## 7 2 1 0
## 8 2 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 4 0
## 14 0 4 0
## 15 0 0 0
## 16 0 1 0
## 17 2 0 0
## 18 0 2 0
## 19 0 0 0
## 20 0 0 0
## 21 1 1 0
## 22 2 1 0
## 23 0 1 1
## 24 0 1 0
## 25 0 2 0
## 26 2 2 0
## 27 0 1 0
## 28 1 0 0
## 29 1 0 0
## 30 1 0 0
## 31 1 2 0
## 32 0 0 0
## 33 1 0 0
## 34 7 0 0
## 35 1 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 1 0
## 39 2 0 0
## 40 1 0 0
## 41 0 1 0
## 42 0 1 0
## 43 0 0 0
## 44 0 0 0
## 45 0 1 0
## 46 1 1 0
## 47 0 0 0
## 48 0 2 0
## 49 0 1 0
## 50 0 0 0
## Beilschmiedia.pendula Brosimum.alicastrum Brosimum.guianense
## 1 4 5 0
## 2 5 2 0
## 3 7 4 0
## 4 5 3 0
## 5 8 2 0
## 6 6 2 0
## 7 5 6 0
## 8 9 4 0
## 9 11 3 0
## 10 14 6 0
## 11 1 5 0
## 12 2 4 0
## 13 0 2 0
## 14 3 4 0
## 15 9 3 0
## 16 4 4 0
## 17 2 5 0
## 18 0 0 0
## 19 2 4 0
## 20 1 0 0
## 21 2 2 0
## 22 0 8 0
## 23 0 2 0
## 24 16 4 0
## 25 5 5 0
## 26 3 3 0
## 27 1 5 0
## 28 3 4 0
## 29 11 2 0
## 30 4 5 0
## 31 2 2 0
## 32 5 8 0
## 33 2 3 0
## 34 5 5 0
## 35 1 1 0
## 36 1 3 0
## 37 2 4 0
## 38 5 8 0
## 39 9 5 0
## 40 5 5 0
## 41 7 6 0
## 42 4 4 0
## 43 10 2 0
## 44 21 2 0
## 45 42 2 0
## 46 0 5 0
## 47 1 1 1
## 48 1 6 0
## 49 11 4 0
## 50 17 4 0
## Calophyllum.longifolium Casearia.aculeata Casearia.arborea
## 1 0 0 1
## 2 2 0 1
## 3 0 0 3
## 4 2 0 2
## 5 1 0 4
## 6 2 0 1
## 7 2 0 2
## 8 2 1 3
## 9 2 0 9
## 10 0 0 7
## 11 0 0 2
## 12 0 0 1
## 13 0 1 0
## 14 4 0 4
## 15 0 0 6
## 16 1 0 1
## 17 0 1 0
## 18 4 0 0
## 19 1 0 4
## 20 2 0 6
## 21 2 1 0
## 22 1 1 0
## 23 1 0 1
## 24 2 0 1
## 25 1 1 3
## 26 0 0 1
## 27 1 2 0
## 28 1 0 2
## 29 1 0 0
## 30 0 1 0
## 31 4 0 1
## 32 0 1 0
## 33 0 0 0
## 34 0 7 8
## 35 1 0 3
## 36 0 1 5
## 37 1 1 1
## 38 1 0 2
## 39 0 0 2
## 40 1 1 4
## 41 0 1 1
## 42 1 1 1
## 43 0 0 0
## 44 2 1 1
## 45 3 0 0
## 46 2 0 2
## 47 0 0 1
## 48 2 0 2
## 49 2 0 0
## 50 0 0 1
## Casearia.commersoniana Casearia.guianensis Casearia.sylvestris
## 1 0 0 2
## 2 0 0 1
## 3 1 0 0
## 4 0 0 0
## 5 1 0 0
## 6 0 0 3
## 7 0 0 1
## 8 0 0 0
## 9 1 0 1
## 10 0 0 1
## 11 0 0 0
## 12 0 0 1
## 13 0 0 1
## 14 0 0 0
## 15 0 0 0
## 16 0 0 2
## 17 0 1 0
## 18 0 0 1
## 19 0 0 1
## 20 0 0 1
## 21 0 0 0
## 22 0 0 2
## 23 0 0 1
## 24 0 0 2
## 25 0 0 1
## 26 0 0 1
## 27 0 0 1
## 28 0 0 0
## 29 0 0 0
## 30 0 0 5
## 31 0 0 0
## 32 0 0 4
## 33 0 0 0
## 34 0 0 1
## 35 0 0 3
## 36 0 0 2
## 37 0 0 1
## 38 0 0 0
## 39 0 0 3
## 40 0 0 2
## 41 0 0 2
## 42 0 0 2
## 43 0 0 0
## 44 0 0 1
## 45 0 0 0
## 46 0 1 3
## 47 0 0 0
## 48 0 0 0
## 49 0 0 1
## 50 0 0 0
## Cassipourea.guianensis Cavanillesia.platanifolia Cecropia.insignis
## 1 2 0 12
## 2 0 0 5
## 3 1 0 7
## 4 1 0 17
## 5 3 0 21
## 6 4 0 4
## 7 4 0 0
## 8 0 0 7
## 9 2 0 2
## 10 1 0 16
## 11 0 0 3
## 12 2 0 2
## 13 9 0 1
## 14 3 0 11
## 15 0 0 24
## 16 1 0 2
## 17 0 0 3
## 18 6 0 0
## 19 2 0 8
## 20 3 0 11
## 21 1 0 2
## 22 2 0 1
## 23 6 0 2
## 24 1 0 3
## 25 2 1 10
## 26 2 3 1
## 27 0 1 3
## 28 3 1 3
## 29 3 0 5
## 30 3 1 14
## 31 0 1 3
## 32 1 1 4
## 33 0 1 6
## 34 1 1 2
## 35 0 1 2
## 36 0 0 6
## 37 2 0 10
## 38 2 1 6
## 39 0 1 4
## 40 0 1 2
## 41 2 0 7
## 42 2 0 2
## 43 2 1 2
## 44 0 0 0
## 45 3 0 5
## 46 1 0 1
## 47 2 2 2
## 48 0 1 0
## 49 0 0 0
## 50 2 0 0
## Cecropia.obtusifolia Cedrela.odorata Ceiba.pentandra Celtis.schippii
## 1 0 0 0 0
## 2 0 0 1 0
## 3 0 0 1 0
## 4 0 0 0 2
## 5 1 0 1 2
## 6 0 0 0 0
## 7 0 0 0 1
## 8 2 0 1 0
## 9 0 0 0 0
## 10 2 0 1 0
## 11 0 0 2 0
## 12 1 0 0 1
## 13 1 0 2 0
## 14 1 0 0 0
## 15 0 1 2 1
## 16 0 0 1 2
## 17 0 0 2 1
## 18 0 0 0 0
## 19 3 0 0 1
## 20 0 0 1 3
## 21 4 0 2 2
## 22 0 0 2 1
## 23 3 0 2 1
## 24 0 0 1 4
## 25 1 0 1 0
## 26 0 0 1 0
## 27 0 0 2 0
## 28 0 0 0 0
## 29 1 0 3 0
## 30 1 0 1 1
## 31 0 1 0 0
## 32 0 0 1 1
## 33 1 0 1 1
## 34 1 0 0 1
## 35 0 0 1 0
## 36 0 0 1 0
## 37 0 0 1 0
## 38 0 0 1 1
## 39 0 0 0 1
## 40 0 0 0 0
## 41 0 0 1 1
## 42 0 0 0 0
## 43 0 0 0 1
## 44 1 0 0 1
## 45 0 0 0 3
## 46 0 0 2 0
## 47 0 0 0 2
## 48 0 0 0 1
## 49 1 0 0 1
## 50 0 0 0 0
## Cespedesia.spathulata Chamguava.schippii Chimarrhis.parviflora
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 1
## 14 0 0 0
## 15 0 0 0
## 16 1 0 0
## 17 0 0 0
## 18 0 2 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 1 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 1 0 0
## 50 0 0 0
## Maclura.tinctoria Chrysochlamys.eclipes Chrysophyllum.argenteum
## 1 0 0 4
## 2 0 0 1
## 3 0 0 2
## 4 0 0 2
## 5 0 0 6
## 6 0 0 2
## 7 0 0 3
## 8 0 0 2
## 9 0 0 4
## 10 0 0 2
## 11 0 0 2
## 12 0 0 1
## 13 0 0 4
## 14 0 0 2
## 15 0 0 1
## 16 0 1 2
## 17 0 0 1
## 18 1 0 0
## 19 0 0 0
## 20 0 0 2
## 21 0 0 2
## 22 0 0 0
## 23 0 0 1
## 24 0 0 2
## 25 0 0 1
## 26 0 0 0
## 27 0 0 1
## 28 0 0 0
## 29 0 0 3
## 30 0 0 1
## 31 0 0 0
## 32 0 0 1
## 33 0 0 2
## 34 0 0 1
## 35 0 0 1
## 36 0 0 3
## 37 0 0 2
## 38 0 0 0
## 39 0 0 2
## 40 0 0 0
## 41 0 1 3
## 42 0 0 2
## 43 0 0 0
## 44 0 0 1
## 45 0 0 2
## 46 0 0 1
## 47 0 0 5
## 48 0 0 1
## 49 0 0 2
## 50 0 0 2
## Chrysophyllum.cainito Coccoloba.coronata Coccoloba.manzinellensis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 2 0
## 6 0 0 0
## 7 1 0 0
## 8 0 1 2
## 9 0 2 0
## 10 0 1 0
## 11 0 0 0
## 12 0 0 0
## 13 0 1 1
## 14 2 0 0
## 15 1 1 0
## 16 0 1 0
## 17 0 0 0
## 18 2 0 2
## 19 1 0 0
## 20 0 2 0
## 21 0 1 0
## 22 1 1 1
## 23 2 1 0
## 24 1 0 0
## 25 2 1 0
## 26 0 0 0
## 27 1 0 0
## 28 0 1 0
## 29 1 0 0
## 30 0 0 2
## 31 0 1 0
## 32 0 0 0
## 33 1 2 0
## 34 2 0 1
## 35 0 0 1
## 36 0 1 1
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 1 0 0
## 41 0 0 1
## 42 0 0 0
## 43 2 0 0
## 44 0 0 0
## 45 1 0 0
## 46 1 0 0
## 47 1 0 1
## 48 0 0 0
## 49 0 1 0
## 50 1 0 0
## Colubrina.glandulosa Cordia.alliodora Cordia.bicolor Cordia.lasiocalyx
## 1 0 2 12 8
## 2 0 3 14 6
## 3 0 3 35 6
## 4 0 7 23 11
## 5 0 1 13 7
## 6 0 1 7 6
## 7 0 2 5 6
## 8 0 0 10 3
## 9 0 0 7 0
## 10 0 2 13 4
## 11 0 3 10 5
## 12 0 0 2 3
## 13 0 2 0 4
## 14 0 1 11 7
## 15 0 1 21 3
## 16 0 1 11 10
## 17 0 0 7 3
## 18 0 2 2 3
## 19 0 5 8 4
## 20 0 0 17 4
## 21 0 0 5 11
## 22 1 0 4 5
## 23 0 0 2 10
## 24 0 0 3 4
## 25 0 0 3 4
## 26 0 1 1 8
## 27 0 0 3 9
## 28 0 0 4 4
## 29 0 2 3 5
## 30 0 2 5 0
## 31 0 2 0 6
## 32 0 0 3 7
## 33 0 0 3 11
## 34 0 2 0 5
## 35 0 0 3 4
## 36 0 0 9 10
## 37 0 6 2 13
## 38 0 2 3 8
## 39 0 0 1 8
## 40 0 0 2 3
## 41 0 1 6 12
## 42 0 0 9 12
## 43 0 1 2 12
## 44 0 0 3 20
## 45 0 0 1 8
## 46 0 6 7 17
## 47 0 1 3 13
## 48 0 0 3 14
## 49 0 0 2 7
## 50 0 1 2 11
## Coussarea.curvigemma Croton.billbergianus Cupania.cinerea Cupania.latifolia
## 1 0 2 0 0
## 2 0 2 0 0
## 3 0 0 0 0
## 4 1 11 0 1
## 5 0 6 0 0
## 6 2 0 0 0
## 7 1 0 0 0
## 8 0 4 0 0
## 9 1 2 0 0
## 10 1 0 0 0
## 11 0 1 0 0
## 12 3 2 0 0
## 13 4 12 0 0
## 14 0 11 0 0
## 15 0 1 0 0
## 16 1 3 0 2
## 17 4 0 0 0
## 18 6 4 0 0
## 19 3 1 1 0
## 20 0 0 0 1
## 21 0 1 0 0
## 22 11 0 0 0
## 23 2 2 0 0
## 24 1 0 0 0
## 25 0 1 0 0
## 26 0 1 0 0
## 27 2 0 0 1
## 28 2 0 0 0
## 29 0 1 0 0
## 30 2 2 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 1
## 35 1 1 0 0
## 36 0 0 0 1
## 37 0 2 0 1
## 38 0 2 0 0
## 39 0 0 0 0
## 40 0 1 0 0
## 41 0 6 0 3
## 42 0 3 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 1 0 0
## 46 5 0 0 1
## 47 1 3 0 0
## 48 1 6 0 0
## 49 0 2 0 0
## 50 0 1 0 0
## Cupania.rufescens Cupania.seemannii Dendropanax.arboreus
## 1 0 2 0
## 2 0 2 3
## 3 0 1 6
## 4 0 0 0
## 5 0 3 5
## 6 0 0 2
## 7 0 1 1
## 8 0 2 6
## 9 0 2 1
## 10 0 0 3
## 11 0 2 2
## 12 0 1 2
## 13 1 2 5
## 14 0 0 2
## 15 0 4 5
## 16 0 0 3
## 17 2 3 2
## 18 1 0 2
## 19 0 1 3
## 20 0 2 1
## 21 0 2 3
## 22 0 0 0
## 23 0 1 1
## 24 0 1 3
## 25 0 1 10
## 26 0 0 0
## 27 0 2 0
## 28 0 0 0
## 29 0 2 2
## 30 0 1 3
## 31 0 0 0
## 32 0 1 0
## 33 0 0 1
## 34 0 0 2
## 35 0 1 0
## 36 0 1 0
## 37 0 0 1
## 38 0 0 1
## 39 0 1 0
## 40 0 0 1
## 41 0 1 1
## 42 0 1 0
## 43 0 0 1
## 44 0 0 0
## 45 0 0 2
## 46 0 1 0
## 47 0 1 0
## 48 0 0 0
## 49 0 0 1
## 50 0 1 1
## Desmopsis.panamensis Diospyros.artanthifolia Dipteryx.oleifera
## 1 0 1 1
## 2 0 1 1
## 3 4 1 3
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 2
## 9 0 0 1
## 10 1 1 2
## 11 0 1 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 1
## 15 0 0 1
## 16 0 0 0
## 17 0 1 0
## 18 0 0 0
## 19 1 0 1
## 20 0 0 2
## 21 0 0 0
## 22 0 2 0
## 23 1 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 1
## 27 0 0 1
## 28 0 0 3
## 29 0 0 1
## 30 0 0 2
## 31 0 1 0
## 32 0 0 1
## 33 1 0 1
## 34 1 0 0
## 35 0 0 0
## 36 1 0 1
## 37 1 0 0
## 38 0 1 0
## 39 1 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 2
## 44 1 0 1
## 45 0 0 2
## 46 0 3 1
## 47 0 2 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 1
## Drypetes.standleyi Elaeis.oleifera Enterolobium.schomburgkii
## 1 2 0 0
## 2 1 0 0
## 3 2 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 3 0 0
## 12 2 0 0
## 13 0 6 0
## 14 0 0 0
## 15 0 0 0
## 16 2 0 0
## 17 3 0 0
## 18 1 9 0
## 19 1 1 1
## 20 0 0 0
## 21 7 0 0
## 22 4 0 0
## 23 0 5 0
## 24 0 0 0
## 25 2 0 0
## 26 8 0 0
## 27 7 0 0
## 28 2 0 0
## 29 4 0 0
## 30 3 0 1
## 31 15 0 0
## 32 6 0 0
## 33 2 0 0
## 34 1 0 0
## 35 1 0 0
## 36 39 0 0
## 37 13 0 0
## 38 12 0 0
## 39 2 0 0
## 40 0 0 0
## 41 21 0 0
## 42 14 0 0
## 43 17 0 0
## 44 8 0 0
## 45 7 0 0
## 46 33 0 0
## 47 12 0 0
## 48 11 0 0
## 49 5 0 0
## 50 12 0 0
## Erythrina.costaricensis Erythroxylum.macrophyllum Eugenia.florida
## 1 0 0 0
## 2 0 1 1
## 3 0 0 0
## 4 0 0 7
## 5 0 0 2
## 6 3 0 0
## 7 0 0 0
## 8 0 1 1
## 9 1 1 1
## 10 0 1 3
## 11 1 0 1
## 12 0 1 1
## 13 0 0 2
## 14 1 0 1
## 15 1 1 0
## 16 1 0 0
## 17 0 0 2
## 18 0 0 2
## 19 0 0 2
## 20 1 1 1
## 21 0 0 1
## 22 0 2 1
## 23 1 1 2
## 24 3 0 1
## 25 0 1 2
## 26 1 0 1
## 27 0 1 1
## 28 0 0 0
## 29 0 0 5
## 30 0 1 3
## 31 0 0 0
## 32 0 0 1
## 33 0 0 1
## 34 0 0 4
## 35 0 1 1
## 36 1 0 2
## 37 1 1 2
## 38 0 0 3
## 39 0 0 4
## 40 0 0 1
## 41 0 0 1
## 42 1 0 2
## 43 2 0 3
## 44 4 0 2
## 45 1 0 1
## 46 0 0 4
## 47 0 0 4
## 48 0 1 1
## 49 2 1 0
## 50 0 1 0
## Eugenia.galalonensis Eugenia.nesiotica Eugenia.oerstediana
## 1 0 0 3
## 2 0 0 2
## 3 0 1 5
## 4 0 0 1
## 5 0 0 5
## 6 0 0 2
## 7 0 5 2
## 8 1 4 3
## 9 0 3 3
## 10 0 0 3
## 11 0 0 6
## 12 1 2 1
## 13 0 2 11
## 14 0 3 1
## 15 0 2 4
## 16 0 0 7
## 17 0 2 3
## 18 0 2 4
## 19 1 1 6
## 20 0 0 2
## 21 1 1 2
## 22 0 1 2
## 23 0 1 2
## 24 0 0 2
## 25 1 0 6
## 26 1 0 1
## 27 0 2 3
## 28 0 1 5
## 29 1 0 1
## 30 0 0 5
## 31 0 0 3
## 32 0 1 2
## 33 0 1 5
## 34 0 3 7
## 35 0 0 6
## 36 0 2 0
## 37 0 0 4
## 38 0 1 10
## 39 0 2 4
## 40 2 2 3
## 41 0 0 7
## 42 0 0 8
## 43 0 3 4
## 44 0 0 3
## 45 0 1 2
## 46 0 1 0
## 47 1 1 2
## 48 0 1 1
## 49 0 2 1
## 50 2 1 2
## Faramea.occidentalis Ficus.colubrinae Ficus.costaricana Ficus.insipida
## 1 14 0 0 0
## 2 36 1 0 0
## 3 39 0 0 0
## 4 39 0 0 0
## 5 22 0 0 0
## 6 16 0 0 0
## 7 38 0 0 0
## 8 41 0 0 0
## 9 33 0 0 0
## 10 42 0 0 0
## 11 17 0 0 0
## 12 38 0 0 0
## 13 39 0 0 1
## 14 31 0 0 0
## 15 31 0 0 0
## 16 27 0 0 0
## 17 42 0 0 0
## 18 35 0 1 0
## 19 42 0 0 0
## 20 21 0 1 0
## 21 17 0 0 0
## 22 58 0 0 0
## 23 26 0 1 1
## 24 24 0 0 0
## 25 35 0 0 0
## 26 31 0 0 0
## 27 51 0 0 0
## 28 58 0 0 0
## 29 54 0 0 0
## 30 61 0 0 0
## 31 30 0 0 0
## 32 60 0 0 0
## 33 53 0 0 0
## 34 49 0 0 0
## 35 37 0 0 0
## 36 32 0 1 0
## 37 65 0 0 0
## 38 80 0 1 0
## 39 43 0 2 0
## 40 41 0 0 1
## 41 22 0 0 0
## 42 25 0 0 0
## 43 15 0 0 0
## 44 15 0 0 0
## 45 10 0 0 0
## 46 22 0 0 0
## 47 27 0 0 0
## 48 15 0 0 0
## 49 11 0 0 0
## 50 7 0 0 0
## Ficus.maxima Ficus.obtusifolia Ficus.popenoei Ficus.tonduzii Ficus.trigonata
## 1 1 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 1 0
## 4 0 0 0 2 0
## 5 0 0 0 1 0
## 6 0 0 0 0 0
## 7 0 0 1 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 1 1 0
## 13 0 0 0 0 0
## 14 0 1 0 0 0
## 15 0 0 0 2 0
## 16 0 0 0 1 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 3 0
## 21 0 0 0 0 0
## 22 1 1 0 0 0
## 23 0 0 0 0 0
## 24 0 2 0 0 0
## 25 0 0 0 1 0
## 26 0 0 0 0 0
## 27 0 0 0 0 0
## 28 0 0 0 0 0
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 1 0 0 1 0
## 32 0 0 0 0 0
## 33 0 1 0 0 0
## 34 0 0 0 0 1
## 35 0 0 0 0 0
## 36 0 0 0 1 0
## 37 0 0 0 1 0
## 38 0 0 0 1 0
## 39 0 0 0 0 0
## 40 0 0 0 0 0
## 41 1 1 0 0 0
## 42 0 0 0 2 1
## 43 0 0 0 2 1
## 44 0 0 1 0 1
## 45 0 0 0 0 0
## 46 0 0 0 0 0
## 47 0 1 0 0 0
## 48 0 0 0 1 0
## 49 0 0 0 1 0
## 50 0 0 0 1 1
## Ficus.yoponensis Garcinia.intermedia Garcinia.madruno Genipa.americana
## 1 1 0 4 0
## 2 0 1 0 0
## 3 0 1 0 1
## 4 0 3 0 0
## 5 0 2 1 0
## 6 1 1 0 0
## 7 1 2 0 1
## 8 0 2 0 0
## 9 0 1 0 1
## 10 0 0 1 1
## 11 0 1 0 0
## 12 0 3 0 0
## 13 0 3 0 1
## 14 0 1 0 1
## 15 0 0 1 0
## 16 0 1 0 2
## 17 0 0 0 1
## 18 0 2 0 1
## 19 0 3 0 0
## 20 0 0 1 0
## 21 0 1 0 0
## 22 0 3 0 0
## 23 0 2 1 0
## 24 0 1 0 0
## 25 1 3 1 0
## 26 0 2 0 1
## 27 0 2 1 0
## 28 0 4 0 0
## 29 0 1 0 1
## 30 0 1 0 0
## 31 0 0 0 0
## 32 0 3 0 0
## 33 0 0 0 0
## 34 0 4 1 0
## 35 1 2 0 0
## 36 0 0 0 1
## 37 0 1 0 0
## 38 0 2 0 0
## 39 0 6 0 1
## 40 0 6 0 0
## 41 0 0 0 2
## 42 0 0 0 0
## 43 0 2 0 0
## 44 0 4 0 0
## 45 0 0 0 1
## 46 0 8 0 1
## 47 1 3 0 1
## 48 0 0 0 0
## 49 0 0 0 1
## 50 0 4 0 3
## Guapira.myrtiflora Guarea.fuzzy Guarea.grandifolia Guarea.guidonia
## 1 3 1 0 2
## 2 1 1 0 6
## 3 0 0 0 2
## 4 1 1 0 5
## 5 1 3 0 3
## 6 7 0 0 4
## 7 3 0 0 4
## 8 1 2 1 0
## 9 1 0 0 1
## 10 1 3 0 5
## 11 1 1 1 5
## 12 3 1 0 3
## 13 2 0 0 4
## 14 0 0 0 3
## 15 1 2 2 0
## 16 2 2 0 7
## 17 1 0 0 2
## 18 0 0 0 2
## 19 3 0 0 2
## 20 1 1 1 5
## 21 3 3 0 7
## 22 3 0 1 4
## 23 1 2 0 6
## 24 7 0 0 8
## 25 2 1 0 6
## 26 2 1 0 6
## 27 4 1 0 12
## 28 2 2 0 8
## 29 2 3 1 3
## 30 2 2 0 2
## 31 2 0 0 9
## 32 0 2 0 11
## 33 4 1 1 27
## 34 2 5 0 16
## 35 1 0 0 3
## 36 2 2 0 10
## 37 1 2 1 24
## 38 5 2 0 12
## 39 3 1 1 9
## 40 4 2 0 6
## 41 1 0 0 18
## 42 1 1 0 8
## 43 1 1 0 16
## 44 0 3 0 8
## 45 2 7 0 11
## 46 2 2 0 4
## 47 2 0 0 10
## 48 4 1 0 13
## 49 1 1 0 20
## 50 0 2 0 14
## Guatteria.dumetorum Guazuma.ulmifolia Guettarda.foliacea Gustavia.superba
## 1 6 0 1 10
## 2 16 0 5 5
## 3 6 0 1 0
## 4 3 1 2 1
## 5 9 0 1 3
## 6 7 0 0 1
## 7 8 0 0 8
## 8 6 0 4 4
## 9 2 0 1 4
## 10 2 0 3 4
## 11 3 0 1 2
## 12 2 0 0 3
## 13 0 2 2 10
## 14 7 1 0 4
## 15 16 1 4 4
## 16 5 1 0 10
## 17 3 0 1 7
## 18 0 1 2 3
## 19 3 1 1 6
## 20 7 2 4 4
## 21 10 0 0 6
## 22 5 0 0 9
## 23 2 1 0 3
## 24 5 1 3 4
## 25 2 2 3 14
## 26 13 0 0 8
## 27 3 2 0 3
## 28 3 1 1 8
## 29 5 1 0 6
## 30 1 2 6 23
## 31 10 0 1 2
## 32 11 0 2 8
## 33 3 0 0 6
## 34 1 5 3 25
## 35 0 5 7 247
## 36 8 2 2 1
## 37 5 0 0 1
## 38 3 0 2 11
## 39 1 2 2 22
## 40 4 0 3 63
## 41 5 1 1 6
## 42 7 0 2 4
## 43 5 1 1 1
## 44 6 0 2 17
## 45 1 0 0 2
## 46 1 0 1 11
## 47 0 0 4 11
## 48 2 1 3 4
## 49 7 1 1 15
## 50 4 0 2 10
## Hampea.appendiculata Hasseltia.floribunda Heisteria.acuminata
## 1 0 5 0
## 2 0 9 0
## 3 1 4 0
## 4 0 11 0
## 5 0 9 1
## 6 0 2 1
## 7 0 7 0
## 8 0 6 0
## 9 2 3 0
## 10 1 4 0
## 11 0 5 0
## 12 1 0 0
## 13 0 1 0
## 14 0 4 1
## 15 0 20 0
## 16 0 7 0
## 17 0 1 0
## 18 0 2 0
## 19 0 3 0
## 20 0 6 0
## 21 0 3 1
## 22 0 5 0
## 23 0 6 0
## 24 1 5 0
## 25 1 3 0
## 26 0 7 1
## 27 0 1 0
## 28 0 3 0
## 29 1 1 0
## 30 0 10 0
## 31 0 2 0
## 32 0 7 0
## 33 0 4 0
## 34 0 1 0
## 35 0 7 0
## 36 1 1 1
## 37 1 3 0
## 38 0 2 0
## 39 0 2 0
## 40 1 3 0
## 41 0 1 1
## 42 1 4 0
## 43 0 6 0
## 44 0 6 0
## 45 1 1 0
## 46 0 0 0
## 47 0 1 0
## 48 0 2 0
## 49 0 7 0
## 50 0 16 0
## Heisteria.concinna Hirtella.americana Hirtella.triandra Hura.crepitans
## 1 4 0 21 0
## 2 5 0 14 0
## 3 4 0 5 0
## 4 6 0 4 0
## 5 4 0 6 0
## 6 8 0 6 2
## 7 2 0 7 1
## 8 5 0 14 1
## 9 1 0 8 0
## 10 5 0 7 0
## 11 5 0 12 0
## 12 12 0 6 2
## 13 4 0 11 1
## 14 0 0 13 0
## 15 3 0 4 0
## 16 2 0 4 3
## 17 13 0 4 0
## 18 8 0 12 0
## 19 7 0 24 0
## 20 1 0 12 0
## 21 1 0 17 0
## 22 9 0 6 2
## 23 5 0 10 5
## 24 7 1 15 0
## 25 3 0 26 0
## 26 4 0 24 4
## 27 1 0 8 2
## 28 4 0 9 2
## 29 5 0 7 5
## 30 0 0 3 1
## 31 6 0 14 3
## 32 6 0 8 7
## 33 5 0 9 2
## 34 4 0 4 5
## 35 2 0 1 0
## 36 4 0 24 2
## 37 6 0 7 3
## 38 15 0 3 2
## 39 12 0 3 2
## 40 3 0 7 3
## 41 6 0 31 2
## 42 4 0 18 3
## 43 10 0 23 1
## 44 6 0 18 6
## 45 18 0 26 7
## 46 15 4 22 5
## 47 6 0 33 7
## 48 3 0 41 7
## 49 8 0 43 1
## 50 11 0 27 2
## Hieronyma.alchorneoides Inga.acuminata Inga.cocleensis Inga.goldmanii
## 1 0 0 2 0
## 2 2 0 4 0
## 3 0 0 4 1
## 4 0 0 6 0
## 5 0 0 0 2
## 6 0 0 0 1
## 7 0 0 1 0
## 8 0 0 6 1
## 9 1 0 4 1
## 10 0 0 5 2
## 11 0 0 0 1
## 12 0 0 0 0
## 13 1 0 2 1
## 14 2 0 3 1
## 15 0 0 2 2
## 16 0 7 0 0
## 17 0 2 2 1
## 18 2 0 1 2
## 19 1 0 2 0
## 20 3 0 2 3
## 21 1 0 0 0
## 22 1 1 3 2
## 23 5 0 1 6
## 24 0 0 0 1
## 25 2 0 0 0
## 26 1 2 0 1
## 27 0 0 0 2
## 28 2 1 0 2
## 29 1 0 0 0
## 30 0 0 0 1
## 31 1 0 0 0
## 32 0 0 0 1
## 33 0 0 0 2
## 34 0 0 0 1
## 35 3 0 0 0
## 36 1 0 0 2
## 37 3 3 0 2
## 38 1 0 0 0
## 39 0 0 0 0
## 40 1 0 0 1
## 41 2 1 0 1
## 42 1 2 0 0
## 43 0 3 0 0
## 44 0 0 0 1
## 45 0 0 0 0
## 46 0 0 2 1
## 47 2 3 0 1
## 48 0 1 0 1
## 49 1 0 0 1
## 50 0 0 0 0
## Inga.laurina Inga.semialata Inga.nobilis Inga.oerstediana Inga.pezizifera
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 2 1 0 0
## 4 0 4 3 0 0
## 5 1 0 1 0 0
## 6 0 0 0 0 0
## 7 0 0 2 0 0
## 8 0 4 2 0 0
## 9 0 1 2 0 0
## 10 0 1 3 0 0
## 11 0 2 0 0 0
## 12 1 1 0 0 0
## 13 1 0 0 0 0
## 14 0 1 0 0 0
## 15 0 8 5 0 0
## 16 0 6 0 0 0
## 17 0 0 1 0 0
## 18 0 1 1 0 0
## 19 1 0 0 0 0
## 20 0 9 2 0 0
## 21 1 1 1 1 0
## 22 0 2 0 0 0
## 23 0 0 0 0 0
## 24 0 2 1 0 0
## 25 0 1 0 0 0
## 26 0 2 2 0 0
## 27 0 1 2 0 0
## 28 0 2 1 0 0
## 29 0 1 0 1 0
## 30 1 1 1 0 0
## 31 0 1 0 0 0
## 32 0 1 2 0 0
## 33 0 0 0 0 0
## 34 0 0 2 0 0
## 35 0 1 0 0 0
## 36 0 3 0 0 0
## 37 0 2 0 0 0
## 38 0 3 1 0 0
## 39 0 4 1 0 0
## 40 0 7 1 0 3
## 41 0 3 2 0 0
## 42 0 7 3 0 0
## 43 0 0 6 0 2
## 44 0 1 2 0 5
## 45 0 3 2 0 3
## 46 3 1 2 0 0
## 47 1 1 3 0 3
## 48 0 4 3 0 0
## 49 0 3 2 0 1
## 50 0 0 4 0 3
## Inga.punctata Inga.ruiziana Inga.sapindoides Inga.spectabilis
## 1 3 0 2 0
## 2 0 0 0 2
## 3 0 0 3 0
## 4 0 0 2 1
## 5 0 0 5 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 1 0
## 9 0 0 1 0
## 10 0 0 0 0
## 11 0 0 0 1
## 12 0 0 2 0
## 13 0 0 0 0
## 14 0 0 4 2
## 15 0 0 1 1
## 16 0 0 0 0
## 17 0 0 1 1
## 18 0 3 1 0
## 19 0 0 3 1
## 20 1 0 1 1
## 21 1 1 1 0
## 22 0 0 2 0
## 23 0 0 0 0
## 24 0 0 4 0
## 25 0 0 2 0
## 26 0 0 0 0
## 27 1 0 1 0
## 28 0 0 4 0
## 29 0 0 0 0
## 30 0 0 2 1
## 31 0 0 2 0
## 32 0 0 2 0
## 33 0 0 1 0
## 34 1 0 3 0
## 35 1 0 2 0
## 36 0 0 2 0
## 37 0 0 3 0
## 38 0 0 1 0
## 39 0 0 0 0
## 40 0 0 1 0
## 41 1 0 2 1
## 42 0 0 3 1
## 43 0 0 1 0
## 44 0 0 1 1
## 45 0 0 0 0
## 46 0 0 1 0
## 47 0 0 1 0
## 48 0 0 3 0
## 49 0 0 2 0
## 50 1 1 2 0
## Inga.umbellifera Jacaranda.copaia Lacistema.aggregatum Lacmellea.panamensis
## 1 0 6 1 1
## 2 0 10 0 0
## 3 0 9 0 0
## 4 1 2 1 2
## 5 0 3 1 2
## 6 0 7 2 1
## 7 0 4 1 3
## 8 2 8 0 2
## 9 2 5 0 0
## 10 1 12 2 0
## 11 0 7 0 0
## 12 0 2 0 1
## 13 0 3 0 1
## 14 0 12 0 2
## 15 1 23 1 1
## 16 0 6 1 0
## 17 0 4 2 1
## 18 0 3 0 3
## 19 1 7 1 4
## 20 0 10 1 1
## 21 0 8 0 1
## 22 0 6 1 0
## 23 0 4 0 2
## 24 0 2 0 0
## 25 0 5 2 1
## 26 0 0 1 0
## 27 0 3 1 1
## 28 0 3 1 0
## 29 0 3 1 1
## 30 0 3 0 2
## 31 1 0 0 0
## 32 0 3 1 0
## 33 1 6 2 1
## 34 0 2 0 1
## 35 1 3 1 1
## 36 1 3 1 1
## 37 0 2 0 0
## 38 0 1 0 3
## 39 0 1 2 1
## 40 0 1 0 1
## 41 0 3 0 1
## 42 0 3 3 1
## 43 0 2 0 1
## 44 1 0 0 0
## 45 0 1 1 0
## 46 0 7 0 2
## 47 1 13 1 2
## 48 0 1 0 2
## 49 0 3 0 0
## 50 0 1 0 0
## Laetia.procera Laetia.thamnia Lafoensia.punicifolia Licania.hypoleuca
## 1 0 0 0 0
## 2 1 1 0 0
## 3 1 1 0 0
## 4 0 0 0 0
## 5 1 0 0 1
## 6 0 0 0 0
## 7 0 0 0 0
## 8 1 2 0 1
## 9 0 1 0 0
## 10 0 1 0 1
## 11 1 1 0 0
## 12 1 0 0 0
## 13 0 3 0 1
## 14 1 3 1 0
## 15 0 2 0 1
## 16 0 0 0 0
## 17 1 1 0 0
## 18 2 0 0 0
## 19 1 3 4 2
## 20 0 2 0 1
## 21 1 0 0 0
## 22 0 0 0 1
## 23 0 1 0 0
## 24 0 2 0 0
## 25 0 1 0 1
## 26 0 0 0 0
## 27 0 1 0 0
## 28 0 0 0 0
## 29 0 0 0 1
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 1
## 36 0 1 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 1
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 0 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 1
## 50 0 0 0 0
## Licania.platypus Lindackeria.laurina Lonchocarpus.heptaphyllus
## 1 0 0 7
## 2 0 0 7
## 3 0 0 3
## 4 0 0 9
## 5 0 0 2
## 6 0 0 1
## 7 1 1 4
## 8 0 0 2
## 9 0 2 2
## 10 0 2 4
## 11 1 0 2
## 12 0 1 3
## 13 0 3 4
## 14 0 4 3
## 15 0 0 2
## 16 0 0 2
## 17 0 2 5
## 18 0 5 3
## 19 0 3 1
## 20 0 2 1
## 21 1 0 2
## 22 0 2 1
## 23 1 2 4
## 24 0 6 2
## 25 0 2 2
## 26 0 1 2
## 27 0 2 0
## 28 0 1 0
## 29 0 2 3
## 30 0 1 7
## 31 0 0 3
## 32 0 2 1
## 33 0 0 1
## 34 0 0 1
## 35 0 2 2
## 36 0 0 4
## 37 0 0 2
## 38 0 0 0
## 39 0 1 1
## 40 0 0 3
## 41 1 2 1
## 42 0 0 1
## 43 0 1 2
## 44 0 0 0
## 45 0 0 1
## 46 0 7 2
## 47 1 3 3
## 48 2 0 1
## 49 2 1 0
## 50 0 1 2
## Luehea.seemannii Macrocnemum.roseum Maquira.guianensis.costaricana
## 1 1 0 4
## 2 0 0 3
## 3 0 0 7
## 4 0 0 7
## 5 1 0 10
## 6 1 0 4
## 7 2 0 3
## 8 0 0 4
## 9 1 0 7
## 10 0 0 3
## 11 4 0 6
## 12 1 0 3
## 13 4 0 2
## 14 1 0 0
## 15 0 0 4
## 16 4 0 4
## 17 1 0 2
## 18 5 0 0
## 19 4 0 1
## 20 0 0 2
## 21 4 0 5
## 22 2 0 2
## 23 6 0 2
## 24 0 0 5
## 25 2 0 4
## 26 1 1 7
## 27 3 1 3
## 28 3 0 2
## 29 1 0 2
## 30 5 0 2
## 31 2 0 4
## 32 3 3 3
## 33 4 0 3
## 34 3 2 3
## 35 6 6 1
## 36 2 0 5
## 37 2 2 0
## 38 2 0 3
## 39 2 0 1
## 40 1 4 0
## 41 2 1 2
## 42 2 0 4
## 43 1 1 4
## 44 1 0 5
## 45 0 0 7
## 46 0 0 0
## 47 1 2 1
## 48 1 1 4
## 49 1 0 5
## 50 0 1 2
## Margaritaria.nobilis Marila.laxiflora Maytenus.schippii Miconia.affinis
## 1 0 1 2 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 1
## 5 1 0 0 0
## 6 0 0 1 0
## 7 0 0 2 0
## 8 0 0 0 1
## 9 0 0 0 0
## 10 0 0 1 1
## 11 0 0 1 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 1
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 1
## 18 0 0 1 0
## 19 0 0 1 0
## 20 0 0 1 1
## 21 0 0 1 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 1 0
## 27 0 0 0 0
## 28 0 0 1 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 1 0 0
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 1
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 1
## 40 0 0 0 0
## 41 0 0 2 0
## 42 0 0 0 0
## 43 0 0 1 0
## 44 0 0 1 0
## 45 0 0 0 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 1 0 0 0
## 49 0 0 1 0
## 50 0 8 1 0
## Miconia.argentea Miconia.elata Miconia.hondurensis Mosannona.garwoodii
## 1 2 0 0 1
## 2 0 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 1 0 0 1
## 6 0 0 0 1
## 7 1 0 0 1
## 8 4 0 0 0
## 9 0 0 0 0
## 10 0 0 0 1
## 11 0 0 0 0
## 12 0 0 0 0
## 13 5 0 0 0
## 14 3 0 1 0
## 15 0 0 1 0
## 16 0 0 0 0
## 17 3 0 0 2
## 18 4 0 0 1
## 19 0 1 2 0
## 20 10 0 1 1
## 21 0 0 0 0
## 22 0 0 2 1
## 23 0 0 0 0
## 24 6 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 2 0 0 0
## 28 0 0 0 0
## 29 2 0 0 0
## 30 8 0 0 0
## 31 0 0 0 1
## 32 1 0 0 0
## 33 0 0 0 0
## 34 3 0 0 0
## 35 3 0 0 0
## 36 0 0 0 0
## 37 2 0 0 0
## 38 1 0 0 0
## 39 1 0 0 0
## 40 0 0 0 0
## 41 1 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 1
## 45 0 0 0 1
## 46 3 0 0 1
## 47 3 0 0 1
## 48 0 0 0 0
## 49 0 0 0 0
## 50 0 0 0 0
## Myrcia.gatunensis Myrospermum.frutescens Nectandra.cissiflora
## 1 1 0 0
## 2 0 0 1
## 3 0 0 2
## 4 0 0 2
## 5 0 0 2
## 6 0 2 0
## 7 0 0 1
## 8 0 0 2
## 9 1 0 3
## 10 0 0 5
## 11 0 0 3
## 12 0 0 0
## 13 0 0 0
## 14 1 0 1
## 15 0 0 0
## 16 0 0 0
## 17 0 0 1
## 18 0 0 0
## 19 1 0 0
## 20 0 0 2
## 21 0 0 1
## 22 0 0 0
## 23 0 0 0
## 24 0 1 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 1
## 29 0 0 0
## 30 0 0 0
## 31 0 1 0
## 32 0 0 0
## 33 0 0 1
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 1
## 42 0 0 3
## 43 0 0 1
## 44 0 0 0
## 45 1 0 0
## 46 0 3 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## Nectandra.lineata Nectandra.purpurea Ochroma.pyramidale Ocotea.cernua
## 1 0 1 1 0
## 2 0 0 0 0
## 3 0 0 0 1
## 4 0 0 0 1
## 5 0 0 0 0
## 6 0 1 0 0
## 7 0 0 0 1
## 8 0 0 3 0
## 9 0 0 0 1
## 10 0 0 0 0
## 11 0 0 0 0
## 12 1 0 0 2
## 13 0 0 0 5
## 14 0 0 0 1
## 15 0 0 1 2
## 16 1 0 0 0
## 17 0 0 0 0
## 18 1 0 0 1
## 19 0 0 0 2
## 20 1 0 0 1
## 21 1 0 0 0
## 22 0 0 0 0
## 23 0 0 0 1
## 24 1 0 0 1
## 25 0 0 0 1
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 1
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 2
## 35 0 0 0 1
## 36 1 0 0 0
## 37 0 0 0 0
## 38 0 1 0 0
## 39 2 0 0 1
## 40 0 0 0 0
## 41 0 0 0 3
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 0 0
## 46 0 1 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 0
## 50 1 0 0 0
## Ocotea.oblonga Ocotea.puberula Ocotea.whitei Oenocarpus.mapora
## 1 0 0 1 22
## 2 0 0 0 21
## 3 1 0 2 14
## 4 2 2 3 23
## 5 0 0 16 17
## 6 0 1 3 19
## 7 0 0 0 20
## 8 0 2 1 20
## 9 0 0 1 18
## 10 1 2 3 20
## 11 3 1 3 17
## 12 0 0 0 19
## 13 0 3 0 17
## 14 1 0 1 22
## 15 1 3 1 11
## 16 0 0 2 15
## 17 0 0 0 31
## 18 0 0 0 24
## 19 0 1 1 24
## 20 0 2 5 19
## 21 2 0 12 11
## 22 1 0 0 24
## 23 0 0 0 22
## 24 0 1 2 24
## 25 1 1 1 14
## 26 0 0 15 6
## 27 3 0 1 11
## 28 1 0 0 10
## 29 0 1 0 16
## 30 1 0 0 12
## 31 0 0 29 7
## 32 2 0 4 5
## 33 0 0 2 10
## 34 0 0 0 9
## 35 1 0 0 4
## 36 2 0 11 24
## 37 0 0 8 15
## 38 2 0 0 11
## 39 1 0 0 4
## 40 1 0 0 2
## 41 2 0 2 11
## 42 1 0 7 19
## 43 1 0 7 11
## 44 1 0 5 10
## 45 1 1 13 8
## 46 0 0 0 36
## 47 1 0 1 28
## 48 1 1 4 15
## 49 1 0 8 4
## 50 0 0 9 12
## Ormosia.amazonica Ormosia.coccinea Ormosia.macrocalyx Pachira.quinata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 1
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 1 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 1 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 1 0
## 28 0 0 0 0
## 29 1 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 1 0
## 34 0 1 0 0
## 35 0 1 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 1 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 1 0 0
## 45 0 0 0 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 0
## 50 0 0 0 0
## Pachira.sessilis Perebea.xanthochyma Cinnamomum.triplinerve
## 1 0 0 0
## 2 0 1 0
## 3 0 0 1
## 4 0 0 0
## 5 0 1 1
## 6 0 0 0
## 7 0 0 2
## 8 0 0 0
## 9 0 8 1
## 10 0 6 0
## 11 0 0 0
## 12 0 0 1
## 13 0 0 0
## 14 0 2 1
## 15 0 1 0
## 16 0 0 1
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 1 0
## 21 0 0 0
## 22 0 0 1
## 23 0 0 1
## 24 0 0 2
## 25 0 0 1
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 9 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 1
## 36 0 1 1
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 1
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## Picramnia.latifolia Piper.reticulatum Platymiscium.pinnatum
## 1 0 0 3
## 2 0 0 3
## 3 1 0 5
## 4 0 0 1
## 5 0 2 1
## 6 0 0 1
## 7 0 0 6
## 8 1 0 0
## 9 0 0 2
## 10 0 0 0
## 11 1 1 0
## 12 0 0 0
## 13 1 0 2
## 14 1 0 2
## 15 0 0 1
## 16 0 0 1
## 17 0 1 2
## 18 0 0 1
## 19 0 0 2
## 20 1 0 0
## 21 0 0 0
## 22 1 0 0
## 23 0 0 1
## 24 0 0 2
## 25 1 0 3
## 26 6 1 1
## 27 3 1 0
## 28 0 0 4
## 29 0 0 0
## 30 0 0 0
## 31 2 0 0
## 32 2 0 2
## 33 0 0 0
## 34 2 1 2
## 35 1 0 1
## 36 2 1 0
## 37 2 0 2
## 38 1 0 1
## 39 0 0 0
## 40 0 0 0
## 41 5 0 0
## 42 1 0 2
## 43 0 0 1
## 44 1 0 1
## 45 1 0 2
## 46 2 0 2
## 47 0 0 1
## 48 4 1 0
## 49 1 0 0
## 50 1 0 0
## Platypodium.elegans Posoqueria.latifolia Poulsenia.armata Pourouma.bicolor
## 1 2 0 24 5
## 2 1 1 16 3
## 3 3 0 28 0
## 4 0 0 15 0
## 5 0 0 25 1
## 6 2 0 15 0
## 7 3 0 8 0
## 8 3 0 13 1
## 9 1 0 5 1
## 10 0 0 24 0
## 11 1 0 22 1
## 12 2 0 8 0
## 13 2 0 0 0
## 14 1 0 13 0
## 15 2 0 16 0
## 16 1 0 32 0
## 17 0 1 2 0
## 18 1 0 0 0
## 19 1 0 4 0
## 20 1 0 15 0
## 21 0 0 44 0
## 22 0 0 5 0
## 23 1 3 1 0
## 24 0 0 6 0
## 25 1 1 4 0
## 26 2 0 22 0
## 27 1 1 9 0
## 28 2 0 1 0
## 29 0 0 2 0
## 30 1 0 8 0
## 31 0 0 24 0
## 32 1 0 2 0
## 33 0 0 4 0
## 34 0 0 1 0
## 35 1 0 0 0
## 36 1 0 14 0
## 37 0 0 6 0
## 38 0 0 1 0
## 39 0 0 1 0
## 40 2 0 2 0
## 41 0 1 11 0
## 42 0 0 26 0
## 43 0 0 55 0
## 44 1 0 55 0
## 45 0 1 57 1
## 46 0 3 0 0
## 47 2 0 4 0
## 48 0 3 23 0
## 49 0 0 39 0
## 50 0 0 43 0
## Pouteria.fossicola Pouteria.reticulata Pouteria.stipitata Prioria.copaifera
## 1 0 5 0 13
## 2 0 7 0 12
## 3 0 3 1 12
## 4 0 6 0 5
## 5 0 5 0 3
## 6 0 4 0 26
## 7 0 4 0 18
## 8 0 4 0 5
## 9 0 3 0 1
## 10 0 0 1 2
## 11 0 5 1 26
## 12 0 4 0 21
## 13 0 5 0 7
## 14 0 5 1 3
## 15 0 4 0 0
## 16 0 3 1 18
## 17 0 5 6 25
## 18 0 17 1 5
## 19 0 10 5 5
## 20 0 1 2 0
## 21 1 1 0 14
## 22 0 2 1 17
## 23 0 6 0 1
## 24 1 4 0 4
## 25 0 9 0 1
## 26 0 3 0 11
## 27 0 2 1 11
## 28 0 2 0 4
## 29 0 2 1 14
## 30 0 3 1 9
## 31 0 3 0 3
## 32 0 4 0 1
## 33 0 3 1 5
## 34 0 3 0 14
## 35 0 4 0 7
## 36 0 3 1 0
## 37 0 2 0 1
## 38 0 4 0 5
## 39 0 4 0 6
## 40 0 5 0 1
## 41 0 3 0 0
## 42 0 4 0 1
## 43 0 4 0 1
## 44 0 2 0 1
## 45 0 2 1 0
## 46 0 4 3 3
## 47 0 7 1 1
## 48 0 5 0 2
## 49 0 2 1 0
## 50 0 1 0 0
## Protium.costaricense Protium.panamense Protium.tenuifolium
## 1 5 2 11
## 2 4 0 8
## 3 1 2 3
## 4 3 3 9
## 5 7 2 3
## 6 1 1 2
## 7 0 0 6
## 8 0 3 7
## 9 4 0 4
## 10 4 1 6
## 11 2 0 4
## 12 0 0 5
## 13 0 0 4
## 14 3 4 5
## 15 4 0 4
## 16 1 0 2
## 17 1 1 2
## 18 0 0 1
## 19 0 2 5
## 20 7 0 9
## 21 2 1 7
## 22 0 2 4
## 23 0 2 3
## 24 0 1 7
## 25 6 4 7
## 26 4 0 6
## 27 2 1 4
## 28 3 1 2
## 29 1 1 6
## 30 4 2 9
## 31 3 0 9
## 32 2 1 3
## 33 1 0 8
## 34 5 3 8
## 35 1 0 1
## 36 3 0 3
## 37 4 0 8
## 38 1 0 10
## 39 3 1 10
## 40 2 0 7
## 41 0 1 6
## 42 3 2 12
## 43 1 0 6
## 44 3 2 8
## 45 1 3 11
## 46 0 0 18
## 47 1 1 27
## 48 1 0 26
## 49 2 0 22
## 50 5 0 23
## Pseudobombax.septenatum Psidium.friedrichsthalianum Psychotria.grandis
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 1 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 1 0 0
## 22 0 0 0
## 23 1 0 2
## 24 1 0 0
## 25 0 0 0
## 26 0 1 0
## 27 1 0 0
## 28 1 0 0
## 29 0 0 0
## 30 1 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 1 0
## 34 0 0 0
## 35 2 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 1 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## Pterocarpus.rohrii Quararibea.asterolepis Quassia.amara Randia.armata
## 1 1 11 0 3
## 2 0 12 0 2
## 3 0 15 0 1
## 4 2 14 0 4
## 5 1 9 0 2
## 6 1 3 0 9
## 7 2 21 0 14
## 8 2 7 0 4
## 9 1 4 0 4
## 10 0 18 0 1
## 11 2 25 0 7
## 12 2 11 0 2
## 13 2 1 0 15
## 14 2 10 0 3
## 15 2 8 0 2
## 16 1 25 0 3
## 17 1 9 0 5
## 18 3 0 0 10
## 19 1 2 0 2
## 20 3 15 0 3
## 21 0 12 0 1
## 22 0 11 0 11
## 23 4 6 0 1
## 24 0 12 0 4
## 25 1 22 0 4
## 26 2 20 0 3
## 27 1 20 0 10
## 28 2 24 0 5
## 29 1 14 0 6
## 30 3 13 0 4
## 31 1 25 0 7
## 32 6 21 0 20
## 33 1 22 0 16
## 34 1 17 0 11
## 35 2 11 0 5
## 36 0 11 0 2
## 37 3 14 1 2
## 38 2 20 0 8
## 39 2 20 0 5
## 40 0 22 0 7
## 41 2 11 0 3
## 42 3 19 0 1
## 43 1 12 0 2
## 44 1 23 0 1
## 45 1 32 0 1
## 46 3 3 3 4
## 47 1 7 0 2
## 48 2 21 0 4
## 49 3 25 0 0
## 50 2 14 0 2
## Sapium.broadleaf Sapium.glandulosum Schizolobium.parahyba Senna.dariensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 1 0 0
## 4 0 0 0 0
## 5 0 2 0 0
## 6 0 0 1 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 1 0 0
## 19 0 1 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 1 0
## 23 0 0 0 0
## 24 0 1 0 0
## 25 0 1 0 0
## 26 0 1 0 0
## 27 0 2 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 1 0 0
## 34 0 1 0 0
## 35 0 1 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 1
## 42 0 0 0 0
## 43 1 0 0 0
## 44 1 0 0 0
## 45 1 0 0 0
## 46 0 1 0 0
## 47 0 0 0 0
## 48 0 1 0 0
## 49 0 1 0 0
## 50 0 1 0 0
## Simarouba.amara Siparuna.guianensis Siparuna.pauciflora Sloanea.terniflora
## 1 14 3 0 1
## 2 6 2 0 0
## 3 16 1 1 2
## 4 8 2 0 2
## 5 7 0 3 3
## 6 7 1 0 2
## 7 13 1 0 1
## 8 14 0 0 2
## 9 12 0 1 1
## 10 17 0 1 2
## 11 7 1 0 1
## 12 9 0 0 1
## 13 2 0 0 1
## 14 4 0 0 1
## 15 6 0 2 0
## 16 8 1 0 0
## 17 2 0 0 1
## 18 2 1 0 0
## 19 6 0 1 1
## 20 8 0 0 0
## 21 4 0 0 1
## 22 5 0 0 0
## 23 1 0 0 0
## 24 3 0 1 0
## 25 10 0 0 0
## 26 5 0 0 1
## 27 16 0 0 0
## 28 7 0 0 1
## 29 3 0 0 1
## 30 8 0 1 1
## 31 5 0 1 2
## 32 5 0 0 2
## 33 2 0 0 2
## 34 2 0 0 1
## 35 0 0 0 0
## 36 9 0 0 3
## 37 6 0 1 3
## 38 3 0 0 4
## 39 1 0 0 1
## 40 4 0 1 1
## 41 1 0 0 1
## 42 4 0 0 7
## 43 0 0 1 2
## 44 0 0 0 1
## 45 3 0 0 5
## 46 3 0 0 2
## 47 4 0 0 4
## 48 0 0 0 3
## 49 4 0 0 2
## 50 3 0 1 5
## Socratea.exorrhiza Solanum.hayesii Sorocea.affinis Spachea.membranacea
## 1 15 0 1 0
## 2 22 0 1 0
## 3 31 0 1 0
## 4 9 0 1 0
## 5 55 1 0 0
## 6 44 0 1 0
## 7 23 0 0 0
## 8 22 0 0 0
## 9 17 0 0 0
## 10 12 2 1 0
## 11 5 0 1 0
## 12 8 0 0 0
## 13 0 0 0 0
## 14 6 0 0 0
## 15 7 1 1 0
## 16 1 2 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 3 0 1 0
## 20 6 0 0 1
## 21 5 1 0 0
## 22 0 0 0 0
## 23 1 1 1 0
## 24 5 0 1 0
## 25 5 0 0 7
## 26 3 0 0 0
## 27 0 1 2 0
## 28 0 0 1 0
## 29 0 0 1 0
## 30 0 0 0 0
## 31 8 0 2 0
## 32 0 1 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 1 0 0
## 36 1 1 0 0
## 37 0 0 0 0
## 38 2 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 3 0 0 0
## 43 7 0 1 0
## 44 5 0 0 0
## 45 9 0 1 0
## 46 0 0 0 0
## 47 0 0 3 0
## 48 0 0 2 0
## 49 2 0 3 0
## 50 4 0 0 0
## Spondias.mombin Spondias.radlkoferi Sterculia.apetala
## 1 1 2 1
## 2 1 0 2
## 3 0 3 0
## 4 1 3 0
## 5 1 5 0
## 6 0 0 1
## 7 0 5 1
## 8 1 0 0
## 9 0 1 0
## 10 2 1 0
## 11 0 1 0
## 12 1 0 0
## 13 10 1 3
## 14 1 0 0
## 15 0 2 0
## 16 0 2 0
## 17 1 0 1
## 18 3 3 2
## 19 0 0 1
## 20 0 3 0
## 21 0 1 0
## 22 0 1 0
## 23 2 2 1
## 24 0 1 0
## 25 0 2 0
## 26 0 0 1
## 27 0 0 0
## 28 0 0 1
## 29 0 2 0
## 30 1 1 1
## 31 0 1 0
## 32 0 1 2
## 33 0 0 0
## 34 0 1 0
## 35 0 1 0
## 36 0 3 0
## 37 0 1 1
## 38 0 0 0
## 39 0 1 0
## 40 0 2 0
## 41 0 3 1
## 42 0 1 1
## 43 0 1 1
## 44 0 0 0
## 45 0 0 1
## 46 0 1 2
## 47 0 0 1
## 48 1 2 0
## 49 1 1 0
## 50 1 1 0
## Swartzia.simplex.var.grandiflora Swartzia.simplex.continentalis
## 1 3 1
## 2 3 4
## 3 0 2
## 4 1 2
## 5 1 1
## 6 9 5
## 7 9 7
## 8 5 0
## 9 5 0
## 10 0 0
## 11 10 5
## 12 13 13
## 13 2 2
## 14 3 3
## 15 1 0
## 16 5 5
## 17 6 4
## 18 9 2
## 19 6 4
## 20 2 0
## 21 2 1
## 22 6 4
## 23 2 4
## 24 4 3
## 25 2 1
## 26 5 2
## 27 6 4
## 28 8 1
## 29 6 4
## 30 3 0
## 31 0 1
## 32 4 3
## 33 13 4
## 34 14 2
## 35 4 1
## 36 0 3
## 37 8 3
## 38 10 2
## 39 5 3
## 40 1 3
## 41 0 0
## 42 3 1
## 43 8 0
## 44 3 2
## 45 1 1
## 46 0 0
## 47 0 1
## 48 4 2
## 49 2 1
## 50 1 1
## Symphonia.globulifera Handroanthus.guayacan Tabebuia.rosea
## 1 0 1 1
## 2 1 0 2
## 3 1 1 1
## 4 1 0 2
## 5 2 0 3
## 6 0 1 0
## 7 0 0 1
## 8 1 0 0
## 9 1 0 0
## 10 0 0 0
## 11 0 0 0
## 12 1 0 0
## 13 0 2 6
## 14 0 1 0
## 15 2 1 0
## 16 1 0 1
## 17 0 1 1
## 18 0 0 0
## 19 0 0 0
## 20 0 0 1
## 21 2 0 2
## 22 0 2 0
## 23 0 0 4
## 24 1 1 0
## 25 1 0 0
## 26 0 0 2
## 27 0 2 0
## 28 0 6 1
## 29 0 1 0
## 30 0 4 3
## 31 0 0 2
## 32 0 0 1
## 33 0 0 5
## 34 0 0 0
## 35 0 1 0
## 36 0 0 2
## 37 0 0 1
## 38 0 0 1
## 39 1 0 4
## 40 0 0 2
## 41 2 0 1
## 42 1 0 1
## 43 0 2 0
## 44 0 1 2
## 45 2 0 1
## 46 0 1 3
## 47 1 0 3
## 48 0 0 5
## 49 1 0 2
## 50 3 1 1
## Tabernaemontana.arborea Tachigali.versicolor Talisia.nervosa
## 1 9 6 0
## 2 5 1 0
## 3 6 3 0
## 4 10 3 0
## 5 16 0 0
## 6 11 1 0
## 7 9 2 0
## 8 8 0 0
## 9 12 3 0
## 10 10 1 0
## 11 4 4 0
## 12 6 2 0
## 13 12 3 1
## 14 22 0 0
## 15 8 5 0
## 16 11 2 0
## 17 3 1 0
## 18 10 3 0
## 19 9 2 0
## 20 13 3 0
## 21 4 1 0
## 22 10 1 0
## 23 5 1 0
## 24 9 3 0
## 25 6 3 0
## 26 4 4 0
## 27 12 2 0
## 28 5 8 0
## 29 2 2 0
## 30 21 1 0
## 31 1 5 0
## 32 4 1 0
## 33 2 1 0
## 34 5 0 0
## 35 2 1 0
## 36 1 1 0
## 37 0 1 0
## 38 4 1 0
## 39 3 2 0
## 40 2 0 0
## 41 1 3 0
## 42 2 1 0
## 43 0 2 0
## 44 1 0 0
## 45 2 2 0
## 46 2 1 0
## 47 1 1 0
## 48 8 0 0
## 49 2 2 0
## 50 7 2 0
## Talisia.princeps Terminalia.amazonia Terminalia.oblonga
## 1 1 1 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 1 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 5 0
## 14 0 2 0
## 15 0 0 0
## 16 0 0 3
## 17 0 0 0
## 18 0 2 0
## 19 0 0 0
## 20 0 1 0
## 21 0 0 1
## 22 0 0 1
## 23 0 6 0
## 24 0 1 0
## 25 0 2 2
## 26 1 0 5
## 27 0 0 10
## 28 1 0 0
## 29 0 1 0
## 30 0 0 0
## 31 0 1 8
## 32 0 0 2
## 33 0 0 0
## 34 0 0 0
## 35 0 2 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 1 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 0 0 0
## 47 0 0 0
## 48 0 0 2
## 49 0 1 9
## 50 0 0 0
## Tetragastris.panamensis Tetrathylacium.johansenii Theobroma.cacao
## 1 5 0 1
## 2 7 0 1
## 3 10 0 0
## 4 10 0 0
## 5 7 0 1
## 6 17 0 0
## 7 8 0 0
## 8 9 0 0
## 9 5 0 0
## 10 6 0 0
## 11 10 0 0
## 12 10 0 0
## 13 6 0 0
## 14 5 0 0
## 15 3 0 0
## 16 6 0 1
## 17 13 0 0
## 18 11 2 0
## 19 9 0 0
## 20 4 0 0
## 21 4 0 0
## 22 9 0 0
## 23 11 2 0
## 24 15 0 1
## 25 7 0 1
## 26 6 0 0
## 27 11 1 0
## 28 13 0 0
## 29 8 0 1
## 30 5 1 0
## 31 5 0 0
## 32 4 0 1
## 33 1 0 0
## 34 2 1 0
## 35 1 0 0
## 36 5 0 0
## 37 9 0 0
## 38 1 0 0
## 39 2 0 0
## 40 1 0 0
## 41 7 0 1
## 42 7 0 0
## 43 6 0 1
## 44 4 0 0
## 45 9 0 0
## 46 20 0 0
## 47 25 0 0
## 48 8 0 0
## 49 4 0 0
## 50 8 0 2
## Thevetia.ahouai Tocoyena.pittieri Trattinnickia.aspera Trema.micrantha
## 1 0 0 3 0
## 2 0 1 1 0
## 3 0 0 1 0
## 4 0 0 0 2
## 5 0 0 2 1
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 2 0
## 9 0 0 2 0
## 10 0 0 1 0
## 11 0 0 0 1
## 12 0 1 0 1
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 1 0
## 16 0 0 1 0
## 17 0 0 3 0
## 18 0 0 0 0
## 19 0 0 3 1
## 20 0 0 0 0
## 21 0 1 1 0
## 22 0 0 0 0
## 23 1 0 0 1
## 24 0 0 1 0
## 25 0 0 0 1
## 26 0 0 0 0
## 27 0 0 1 0
## 28 0 0 1 1
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 1 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 4 0
## 37 0 0 1 0
## 38 0 0 0 0
## 39 0 0 0 3
## 40 0 0 1 0
## 41 1 0 0 0
## 42 0 0 0 1
## 43 0 0 0 0
## 44 0 0 0 1
## 45 0 0 0 0
## 46 0 0 7 0
## 47 0 0 1 0
## 48 0 0 1 0
## 49 0 0 0 0
## 50 0 1 1 1
## Trichanthera.gigantea Trichilia.pallida Trichilia.tuberculata
## 1 0 0 18
## 2 0 1 27
## 3 0 0 28
## 4 0 1 35
## 5 0 0 15
## 6 0 0 31
## 7 0 1 27
## 8 0 0 36
## 9 0 0 65
## 10 0 0 46
## 11 0 2 41
## 12 0 1 46
## 13 0 8 35
## 14 0 1 33
## 15 0 6 33
## 16 0 3 25
## 17 0 2 36
## 18 0 5 23
## 19 0 1 44
## 20 0 10 33
## 21 0 0 23
## 22 0 1 42
## 23 0 1 23
## 24 1 2 38
## 25 1 6 26
## 26 0 1 15
## 27 0 3 20
## 28 0 0 43
## 29 0 0 47
## 30 0 4 33
## 31 0 1 33
## 32 0 8 50
## 33 0 1 37
## 34 0 2 31
## 35 0 1 30
## 36 0 0 28
## 37 0 4 33
## 38 0 1 59
## 39 0 0 79
## 40 0 2 97
## 41 0 0 19
## 42 0 0 15
## 43 0 0 36
## 44 0 0 26
## 45 0 0 26
## 46 0 0 16
## 47 0 1 16
## 48 0 0 19
## 49 0 1 26
## 50 0 0 18
## Trichospermum.galeottii Triplaris.cumingiana Trophis.caucana
## 1 0 0 2
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 2
## 6 0 1 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 1 0
## 11 0 1 2
## 12 0 3 0
## 13 0 13 0
## 14 0 7 0
## 15 0 0 0
## 16 0 2 0
## 17 0 3 1
## 18 0 6 0
## 19 0 1 0
## 20 1 0 0
## 21 0 2 1
## 22 0 1 1
## 23 0 11 0
## 24 0 1 0
## 25 0 3 0
## 26 0 2 1
## 27 0 3 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 1 0
## 32 0 13 0
## 33 0 11 0
## 34 0 11 0
## 35 0 1 0
## 36 0 0 0
## 37 0 3 0
## 38 0 6 0
## 39 0 4 0
## 40 0 5 0
## 41 0 1 0
## 42 0 0 0
## 43 0 1 4
## 44 0 6 1
## 45 0 5 4
## 46 0 1 0
## 47 0 3 0
## 48 0 6 3
## 49 0 4 8
## 50 0 4 3
## Trophis.racemosa Turpinia.occidentalis Unonopsis.pittieri Virola.multiflora
## 1 1 0 1 0
## 2 1 1 5 0
## 3 0 1 12 0
## 4 1 4 3 0
## 5 0 2 4 0
## 6 0 1 3 2
## 7 1 0 3 0
## 8 0 1 2 0
## 9 0 1 5 0
## 10 2 1 9 1
## 11 0 1 4 0
## 12 0 1 0 1
## 13 0 0 0 0
## 14 1 0 3 0
## 15 0 5 11 0
## 16 2 2 1 1
## 17 0 0 0 0
## 18 2 0 0 0
## 19 3 0 4 0
## 20 0 5 10 0
## 21 0 1 6 2
## 22 0 1 1 0
## 23 1 0 1 0
## 24 1 2 3 0
## 25 2 0 3 0
## 26 1 0 4 1
## 27 1 7 0 0
## 28 0 1 0 0
## 29 0 3 5 0
## 30 2 1 1 1
## 31 1 0 3 5
## 32 2 0 4 0
## 33 1 2 5 0
## 34 0 1 1 0
## 35 0 0 0 0
## 36 0 2 9 2
## 37 0 5 8 2
## 38 0 0 2 0
## 39 0 2 0 0
## 40 1 1 0 0
## 41 0 2 4 2
## 42 0 0 5 3
## 43 1 0 5 0
## 44 0 0 3 0
## 45 0 0 4 0
## 46 0 0 0 0
## 47 2 0 0 0
## 48 2 0 0 1
## 49 0 0 2 0
## 50 0 1 4 1
## Virola.sebifera Virola.surinamensis Vismia.baccifera Vochysia.ferruginea
## 1 17 4 0 0
## 2 12 3 0 0
## 3 11 2 0 0
## 4 16 2 0 0
## 5 31 6 0 0
## 6 19 1 0 0
## 7 8 1 0 0
## 8 19 1 0 0
## 9 16 1 0 0
## 10 17 2 0 0
## 11 6 4 0 0
## 12 6 2 0 2
## 13 0 0 0 3
## 14 17 1 0 1
## 15 16 4 0 0
## 16 24 5 0 0
## 17 5 0 0 1
## 18 0 2 0 2
## 19 12 1 0 0
## 20 15 4 0 0
## 21 15 10 0 0
## 22 7 1 0 0
## 23 9 1 0 0
## 24 12 1 0 0
## 25 12 4 0 0
## 26 20 12 0 0
## 27 12 1 0 0
## 28 18 1 0 0
## 29 9 1 0 0
## 30 8 3 0 0
## 31 20 6 0 0
## 32 18 4 0 0
## 33 16 3 0 0
## 34 7 1 0 0
## 35 11 0 0 0
## 36 14 5 0 0
## 37 14 4 1 0
## 38 11 0 0 0
## 39 7 2 0 0
## 40 11 1 0 0
## 41 12 7 0 0
## 42 12 5 0 0
## 43 14 8 0 0
## 44 10 1 0 0
## 45 13 7 0 0
## 46 0 0 0 2
## 47 11 1 0 1
## 48 7 5 0 0
## 49 6 16 0 0
## 50 14 7 0 0
## Xylopia.macrantha Zanthoxylum.ekmanii Zanthoxylum.juniperinum
## 1 1 3 0
## 2 0 4 0
## 3 0 8 1
## 4 0 13 1
## 5 0 3 0
## 6 0 1 0
## 7 0 2 0
## 8 0 4 0
## 9 0 13 1
## 10 4 7 10
## 11 1 1 0
## 12 0 0 0
## 13 1 5 2
## 14 0 12 3
## 15 3 5 6
## 16 1 2 1
## 17 2 1 0
## 18 1 0 2
## 19 1 3 1
## 20 1 4 2
## 21 15 7 2
## 22 0 1 1
## 23 0 0 0
## 24 0 0 0
## 25 0 4 3
## 26 15 1 1
## 27 8 4 1
## 28 0 0 0
## 29 0 2 0
## 30 0 1 0
## 31 23 0 1
## 32 4 4 0
## 33 8 3 0
## 34 1 0 0
## 35 0 0 0
## 36 20 2 1
## 37 1 5 0
## 38 1 2 1
## 39 0 1 1
## 40 0 0 0
## 41 9 3 0
## 42 14 4 0
## 43 2 1 0
## 44 1 2 0
## 45 0 2 1
## 46 3 1 1
## 47 1 2 1
## 48 1 5 0
## 49 0 1 0
## 50 0 0 0
## Zanthoxylum.panamense Zanthoxylum.setulosum Zuelania.guidonia
## 1 2 0 0
## 2 2 0 0
## 3 2 0 0
## 4 5 0 1
## 5 5 0 0
## 6 3 0 2
## 7 0 0 0
## 8 2 0 0
## 9 4 0 0
## 10 4 0 0
## 11 1 0 0
## 12 0 0 0
## 13 0 0 0
## 14 1 0 0
## 15 2 0 0
## 16 0 0 0
## 17 1 0 1
## 18 1 0 1
## 19 3 0 0
## 20 1 0 0
## 21 0 0 0
## 22 1 0 0
## 23 0 0 0
## 24 0 0 0
## 25 1 0 0
## 26 0 0 0
## 27 2 0 0
## 28 0 0 1
## 29 0 0 0
## 30 3 0 0
## 31 1 0 0
## 32 2 0 0
## 33 1 0 0
## 34 3 0 0
## 35 1 0 0
## 36 0 0 0
## 37 4 0 0
## 38 1 0 0
## 39 1 0 0
## 40 0 0 0
## 41 1 1 0
## 42 1 0 1
## 43 0 0 0
## 44 0 0 0
## 45 0 0 0
## 46 1 0 0
## 47 0 0 1
## 48 1 0 2
## 49 1 0 0
## 50 2 0 0Description from package
A data frame with 50 plots (rows) of 1 hectare with counts of trees on each plot with total of 225 species (columns). Full Latin names are used for tree species. The names were updated against http://www.theplantlist.org and Kress et al. (2009) which allows matching 207 of species against doi: 10.5061/dryad.63q27 (Zanne et al., 2014).
EDA
Metadata analysis
BCI.env %>% str## 'data.frame': 50 obs. of 9 variables:
## $ UTM.EW : num 625754 625754 625754 625754 625754 ...
## $ UTM.NS : num 1011569 1011669 1011769 1011869 1011969 ...
## $ Precipitation: int 2530 2530 2530 2530 2530 2530 2530 2530 2530 2530 ...
## $ Elevation : int 120 120 120 120 120 120 120 120 120 120 ...
## $ Age.cat : Factor w/ 2 levels "c2","c3": 2 2 2 2 2 2 2 2 2 2 ...
## $ Geology : Factor w/ 1 level "Tb": 1 1 1 1 1 1 1 1 1 1 ...
## $ Habitat : Factor w/ 5 levels "OldHigh","OldLow",..: 3 2 2 2 3 2 2 2 2 2 ...
## $ Stream : Factor w/ 2 levels "No","Yes": 2 2 1 1 1 1 2 2 1 1 ...
## $ EnvHet : num 0.627 0.394 0 0 0.461 ...unique(paste(BCI.env$UTM.EW, BCI.env$UTM.NS))## [1] "625753.967 1011568.985" "625753.967 1011668.985" "625753.967 1011768.985"
## [4] "625753.967 1011868.985" "625753.967 1011968.985" "625853.967 1011568.985"
## [7] "625853.967 1011668.985" "625853.967 1011768.985" "625853.967 1011868.985"
## [10] "625853.967 1011968.985" "625953.967 1011568.985" "625953.967 1011668.985"
## [13] "625953.967 1011768.985" "625953.967 1011868.985" "625953.967 1011968.985"
## [16] "626053.967 1011568.985" "626053.967 1011668.985" "626053.967 1011768.985"
## [19] "626053.967 1011868.985" "626053.967 1011968.985" "626153.967 1011568.985"
## [22] "626153.967 1011668.985" "626153.967 1011768.985" "626153.967 1011868.985"
## [25] "626153.967 1011968.985" "626253.967 1011568.985" "626253.967 1011668.985"
## [28] "626253.967 1011768.985" "626253.967 1011868.985" "626253.967 1011968.985"
## [31] "626353.967 1011568.985" "626353.967 1011668.985" "626353.967 1011768.985"
## [34] "626353.967 1011868.985" "626353.967 1011968.985" "626453.967 1011568.985"
## [37] "626453.967 1011668.985" "626453.967 1011768.985" "626453.967 1011868.985"
## [40] "626453.967 1011968.985" "626553.967 1011568.985" "626553.967 1011668.985"
## [43] "626553.967 1011768.985" "626553.967 1011868.985" "626553.967 1011968.985"
## [46] "626653.967 1011568.985" "626653.967 1011668.985" "626653.967 1011768.985"
## [49] "626653.967 1011868.985" "626653.967 1011968.985"All positions are unique. Lets draw tham:
BCI.env %>%
ggplot(aes(UTM.EW, UTM.NS, color = 1:50)) +
geom_point() +
theme_minimal() +
scale_color_viridis("ID")
cat("Precipitation: ", unique(BCI.env$Precipitation), "\n")## Precipitation: 2530cat("Elevation: ", unique(BCI.env$Elevation), "\n")## Elevation: 120cat("Age.cat: ", unique(BCI.env$Age.cat), "\n")## Age.cat: 2 1cat("Geology: ", unique(BCI.env$Geology), "\n")## Geology: 1cat("Habitat: ", unique(BCI.env$Habitat), "\n")## Habitat: 3 2 4 1 5cat("Stream: ", unique(BCI.env$Stream), "\n")## Stream: 2 1cat("EnvHet: ", unique(BCI.env$EnvHet), "\n")## EnvHet: 0.6272 0.3936 0 0.4608 0.0768 0.3808 0.2112 0.4032 0.6624 0.1472 0.6592 0.2688 0.624 0.4352 0.608 0.3648 0.3328 0.6528 0.6144 0.4928 0.7264 0.6208 0.5568 0.3424 0.4992 0.6368Precipitation, elevation and geology is the same for all samples. So it is worth to exclude them
BCI.env <- BCI.env[,-c(3,4,6)]Visualize all other distributions:
## Warning in geom_histogram(aes(Age.cat), stat = "count", show.legend = F, :
## Ignoring unknown parameters: `binwidth`, `bins`, and `pad`## Warning in geom_histogram(aes(Habitat), stat = "count", show.legend = F, :
## Ignoring unknown parameters: `binwidth`, `bins`, and `pad`## Warning in geom_histogram(aes(Stream), stat = "count", show.legend = F, :
## Ignoring unknown parameters: `binwidth`, `bins`, and `pad`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Most of samples (49 out of 50) have age category c3. So it might be
worth to exclude one with age category c2 to reduce the noise in samples
when performing statistical transformations in future
#rmV <- which(BCI.env$Age.cat == "c2")
#BCI.env <- BCI.env[-rmV,]
#BCI <- BCI[-rmV,]How independent are Stream,Habitat and EnvHet?
BCI.env %>%
ggplot(aes(y = EnvHet, "", color = EnvHet)) +
geom_beeswarm() +
facet_grid(Stream~Habitat) +
theme_minimal() +
scale_color_viridis(direction = -1) +
ylab("amount") + xlab("")
There is no samples with stream presence and several habitat parameters.
Also, there is lack of swamp and young habitat samples.
Making tidy table
df <- BCI %>%
rownames_to_column("id") %>%
pivot_longer(cols = 2:226) %>%
as_tibble() %>%
mutate_at("value", as.numeric) %>%
mutate_at(c("id", "name"), as.factor) %>%
left_join(
BCI.env %>%
rownames_to_column("id"),
by = "id")
df$id <- fct_inseq(df$id)Sample analysis
df %>%
ggplot(aes(y = id, log10(value+1), fill = id)) +
geom_boxplot(show.legend = F, linewidth = 0.1, outliers = F) +
geom_jitter(show.legend = F, size = 0.1, alpha = 0.1) +
scale_fill_viridis_d() +
theme_minimal()
Most of values are 0 or 1, so first of all lets check the amount of
species per sample
df %>%
mutate("pres" = (value > 0)/225) %>%
summarise(pres = sum(pres), .by = c(UTM.EW, UTM.NS)) %>%
ggplot(aes(x = UTM.EW, y = UTM.NS, fill = pres)) +
geom_tile() +
scale_fill_viridis("% of all species presented", direction = -1) +
theme_minimal()
All samples are pretty consistent by amount of different species in each
of them. And how about overall amount of trees?
df %>%
summarise(N = sum(value), .by = c(UTM.EW, UTM.NS)) %>%
ggplot(aes(x = UTM.EW, y = UTM.NS, fill = N)) +
geom_tile() +
scale_fill_viridis("trees", direction = -1) +
theme_minimal() +
theme(legend.position = "bottom")
What is the variance behind this values?
g1 <- df %>%
summarise(N = sum(value), .by = c(UTM.EW, UTM.NS)) %>%
ggplot(aes(x = N)) +
geom_density() +
xlab("number of trees") + ylab("density") +
scale_fill_viridis() +
theme_minimal()
g2 <- df %>%
mutate("pres" = (value > 0)/225) %>%
summarise(pres = sum(pres), .by = c(UTM.EW, UTM.NS)) %>%
ggplot(aes(x = pres)) +
geom_density() +
xlab("presence of trees") + ylab("") +
scale_fill_viridis() +
theme_minimal()
ggarrange(g1, g2, nrow = 1)
All samples are pretty normal by trees overall presence, but one sample
is with excepted amount of trees. Lets explore it:
tmp <- df %>%
summarise(N = sum(value), .by = id)
tmp <- tmp$id[tmp$N > 550] %>% as.character
top <- df[df$id == tmp, c("value", "name")]
top <- top$name[order(top$value, decreasing = T)][1:20]
df %>%
summarise(value = mean(value), id = "mean", .by = name) %>%
rbind(df[df$id == tmp, c("value", "name", "id")]) %>%
subset(name %in% top) %>%
ggplot(aes(value, fct_reorder(name,value), fill = id)) +
geom_col(position = "dodge") +
theme_minimal() + ylab("species")
The biggest differences occurce because of exceptionally high level of
Gustavia superba and Alsesis blackania. It is better to remove this
sample in future Q-mode analysis
BTW, while adjusting nice legend I suddenly revealed a true masterpiece
df %>%
summarise(N = sum(value), .by = c(UTM.EW, UTM.NS)) %>%
ggplot(aes(x = N, color = N)) +
geom_segment(aes(yend = 0, xend = ..density..), stat = "density") +
xlab("density") + ylab("") +
scale_fill_viridis() +
theme_minimal()## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
We can see that in the center there are less trees, than on the north east periphery. The amount of trees does not seems to correlate to their diversity.
So, in this way I perform some R-mode analysis. And what about Q-mode analysis? Is it better to use normalized data for the process? Possible yes, but let’s check it
Species analysis
sum_sample <- df %>% summarise(total = sum(value), .by = c(id))
df <- df %>%
left_join(sum_sample, by = "id") %>%
mutate("am" = value/total)Let’s check, what is better to use in further standartization process
sum_tree1 <- df %>% summarise(mt = mean(value),
st = sd(value),
.by = c(name))
sum_tree2 <- df %>% summarise(mt = mean(am),
st = sd(am),
.by = c(name))Mean visualization
Standardized using absolute value
sum_tree <- sum_tree1[order(sum_tree1$mt),]
sum_tree$name <- fct_inorder(sum_tree$name)
sum_tree %>%
mutate(vmax = mt + st) %>%
mutate(vmin = ifelse(mt - st > 0, mt - st, 0)) %>%
ggplot(aes(y = name, mt, color = name, text = name)) +
geom_pointrange(aes(xmin = vmin, xmax = vmax),
show.legend = F, size = 0.1, alpha = 0.2) +
theme_minimal() +
ylab("species") +
theme(panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.y = element_blank()) +
scale_color_viridis_d(direction = -1) +
xlab("number")
Standardized using relative amount in sample
sum_tree <- sum_tree2[order(sum_tree2$mt),]
sum_tree$name <- fct_inorder(sum_tree$name)
sum_tree %>%
mutate(vmax = mt + st) %>%
mutate(vmin = ifelse(mt - st > 0, mt - st, 0)) %>%
ggplot(aes(y = name, mt, color = name, text = name)) +
geom_pointrange(aes(xmin = vmin, xmax = vmax),
show.legend = F, size = 0.1, alpha = 0.2) +
theme_minimal() +
ylab("species") +
theme(panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
axis.text.y = element_blank()) +
scale_color_viridis_d(direction = -1) +
xlab("amount")
Using absolute values might be slightly better
Values vizualization
Is the real values normally distribured across all species?
df <- df %>%
left_join(sum_tree1, by = "name")
ggplot(df, aes(am)) +
geom_jitter(aes(color = mt, y = 200), height = 200, size = 0.1) +
geom_density(color = "red") +
scale_color_viridis("mean per species", direction = -1) +
theme_minimal() +
ylab("density")
I can’t hold on beyond using beeswarm here)
ggplot(df[df$value!=0,], aes(am, y = "", color = name)) +
geom_beeswarm(show.legend = F, size = 0.2) +
theme_minimal() +
scale_color_viridis_d()
One really bad outlier with the exceptional amount of one tree (35) might not be used in further sample analysis, as well as the 33rd sample with c2
Full analysis
Let’s check independence of influencing factors:
ggplot(df[df$am!=0,], aes(y = am, "", color = EnvHet)) +
geom_beeswarm(size = 0.5, alpha = 0.5) +
facet_grid(Stream~Habitat) +
theme_minimal() +
scale_color_viridis(direction = -1) +
ylab("amount") + xlab("")
Seems that for further Q-mode analysis is also better to exclude all young samples because of outliers. Also, swamp, young habitats and samples with presence of stream have high environmental heterogenity. So, env.het is a type of habitat measure..
Let’s move on!
NMDS
Let’s use Bray-Curtis distance
NMDS <- metaMDS(BCI, k = 2, dist = "bray", autotransform = FALSE)## Run 0 stress 0.1741506
## Run 1 stress 0.1741506
## ... Procrustes: rmse 3.324596e-05 max resid 0.0002059128
## ... Similar to previous best
## Run 2 stress 0.1792031
## Run 3 stress 0.2012435
## Run 4 stress 0.1900287
## Run 5 stress 0.199913
## Run 6 stress 0.1880655
## Run 7 stress 0.1880657
## Run 8 stress 0.1855482
## Run 9 stress 0.1871092
## Run 10 stress 0.178791
## Run 11 stress 0.1741506
## ... Procrustes: rmse 2.611141e-05 max resid 0.0001658493
## ... Similar to previous best
## Run 12 stress 0.224473
## Run 13 stress 0.183808
## Run 14 stress 0.1741506
## ... New best solution
## ... Procrustes: rmse 3.509224e-06 max resid 1.405608e-05
## ... Similar to previous best
## Run 15 stress 0.1997638
## Run 16 stress 0.1741506
## ... Procrustes: rmse 5.281221e-06 max resid 2.250866e-05
## ... Similar to previous best
## Run 17 stress 0.1921145
## Run 18 stress 0.1838079
## Run 19 stress 0.1863736
## Run 20 stress 0.1741506
## ... Procrustes: rmse 5.457219e-06 max resid 2.16105e-05
## ... Similar to previous best
## *** Best solution repeated 3 timesstressplot(NMDS)
A lot of data, so looks good ## Plain
ordiplot(NMDS,type = "n")
orditorp(NMDS, display="species", labels = " ", col = "#00297a70", cex = 0.2, pch = 1)
orditorp(NMDS, display="sites",labels = " ",col = "#007a3270", cex = 0.2, pch = 10)
## Add legend
Env.Het
ordiplot(NMDS,type = "n")
ordisurf(NMDS,BCI.env$EnvHet,main="",col="forestgreen", cex = 0.2)##
## Family: gaussian
## Link function: identity
##
## Formula:
## y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
##
## Estimated degrees of freedom:
## 3.1 total = 4.1
##
## REML score: 0.1980068orditorp(NMDS, display="species", labels = " ", col = "#00297a70", cex = 0.1, pch = 1)
### Stream
tmp <- BCI.env$Stream %>% factor
cols <- match(tmp, levels(tmp), 1:length(levels(tmp)))
ordiplot(NMDS,type = "n")
ordiellipse(NMDS,BCI.env$Stream,main="",cex = 0.2, col = c("darkgreen", "darkred"))
orditorp(NMDS, display="sites",labels = " ",col = cols, cex = 0.2, pch = 10)
Stream presented samples are on the periphery of the NMDS, but does not
form special group
Habitat
tmp <- BCI.env$Habitat %>% factor
cols <- match(tmp, levels(tmp), 1:length(levels(tmp)))
ordiplot(NMDS,type = "n")
ordiellipse(NMDS,BCI.env$Habitat,main="",cex = 0.2, col = c("darkgreen", "darkred"))## Warning in chol.default(cov, pivot = TRUE): the matrix is either rank-deficient
## or not positive definite
## Warning in chol.default(cov, pivot = TRUE): the matrix is either rank-deficient
## or not positive definite
Habitats vary a little, so we can see there the influence of this
factor.
NMDS_sm <- data.frame(BCI.env, scores(NMDS, display = "sites"))
NMDS_sp <- data.frame(scores(NMDS, display = "species"))ggplot2
g1 <- ggplot() +
geom_point(data = NMDS_sm, aes (x = NMDS1, y = NMDS2, text = 1:50,
colour = Habitat, shape = Stream, size = EnvHet), alpha = 0.5) +
theme_minimal() +
scale_color_viridis_d()## Warning in geom_point(data = NMDS_sm, aes(x = NMDS1, y = NMDS2, text = 1:50, :
## Ignoring unknown aesthetics: textg1
#ggplotly(g1)Seems like 3 clusters (or 2 with outliers) with unknown properties.
What about species? Let’s annotate them by presence in samples
pres <- df %>%
summarise(presence = sum(value > 0)/50, .by = name)
g2 <- NMDS_sp %>%
rownames_to_column("name") %>%
left_join(pres, by = "name") %>%
ggplot(aes(x = NMDS1, y = NMDS2, text = name, color = presence)) +
geom_point(alpha = 0.5, size = 0.7) +
theme_minimal() +
scale_color_viridis("presence", direction = -1)
ggplotly(g2)As it could be predicted, low frequency trees are outliers on NMDS
Adding environmental parameters
ef <- envfit(NMDS, BCI.env)
ef$vectors %>% print## NMDS1 NMDS2 r2 Pr(>r)
## UTM.EW -0.17490 0.98459 0.7548 0.001 ***
## UTM.NS 0.88876 -0.45836 0.0255 0.531
## EnvHet -0.16403 0.98646 0.0131 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999cat("----\n")## ----ef$factors %>% print## Centroids:
## NMDS1 NMDS2
## Age.catc2 0.1048 0.1077
## Age.catc3 -0.0021 -0.0022
## HabitatOldHigh 0.1831 0.1558
## HabitatOldLow -0.0004 -0.0574
## HabitatOldSlope -0.2717 0.0330
## HabitatSwamp 0.3630 -0.2911
## HabitatYoung 0.5401 0.2154
## StreamNo 0.0140 0.0116
## StreamYes -0.0859 -0.0714
##
## Goodness of fit:
## r2 Pr(>r)
## Age.cat 0.0044 0.797
## Habitat 0.4903 0.001 ***
## Stream 0.0195 0.405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999Habitat fits awesome the differences in data, while other parameters seems to be not correlated with samples tree amounts. Interestingly, horizontal axis UTM.EW also influence on sample properties.
Visualization:
env <- BCI.env
env[,3:5] <- env[,3:5] %>% sapply(match,
env[,3:5] %>% unlist %>% factor %>% levels,
1:9)
par(mfrow = c(2, 3))
o1 <- ordisurf(NMDS, env$UTM.EW, method = "REML")
o2 <- ordisurf(NMDS, env$UTM.NS, method = "REML")
o3 <- ordisurf(NMDS, env$Habitat, method = "REML")
o4 <- ordisurf(NMDS, env$Stream, method = "REML")
o5 <- ordisurf(NMDS, env$EnvHet, method = "REML")
par(mfrow = c(1, 1))
1st NMDS axis is mostly UTM.EW, while 2nd is mostly Habitat
explained.
cat("UTM.EW:")## UTM.EW:o1 %>% summary %>% print##
## Family: gaussian
## Link function: identity
##
## Formula:
## y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 626203.97 17.67 35445 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(x1,x2) 6.077 9 23.92 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.815 Deviance explained = 83.8%
## -REML = 316.05 Scale est. = 15606 n = 50cat("\n#########################\n\n")##
## #########################cat("Habitat:")## Habitat:o3 %>% summary %>% print##
## Family: gaussian
## Link function: identity
##
## Formula:
## y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.28000 0.09976 42.9 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(x1,x2) 6.407 9 3.952 7.45e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.421 Deviance explained = 49.6%
## -REML = 61.473 Scale est. = 0.4976 n = 50Fortifying objects:
NMDS_ef <- fortify(ef)
fortify_ordisurf <- function(model) {
xy <- expand.grid(x = model$grid$x, y = model$grid$y)
xyz <- cbind(xy, c(model$grid$z))
names(xyz) <- c("x", "y", "z")
return(na.omit(xyz))
}
NMDS_os_EW <- fortify_ordisurf(o1)
NMDS_os_Habitat <- fortify_ordisurf(o3)Let’s check real features correlation
g1 <- NMDS_sm %>%
ggplot(aes(UTM.EW, NMDS2, color = NMDS1)) +
geom_smooth(alpha = 0.1, color = "red", linetype = 2, method = "lm") +
geom_point() +
theme_minimal() +
theme(legend.position = "bottom") +
scale_color_viridis("NMDS1")
g2 <- NMDS_sm %>%
ggplot(aes(Habitat, NMDS1, color = NMDS2)) +
geom_boxplot(alpha = 0.1, aes(fill = Habitat), show.legend = F) +
geom_beeswarm() +
theme_minimal() +
scale_color_viridis("NMDS2") +
theme(legend.position = "bottom") +
scale_fill_viridis_d()
ggarrange(g1,g2, nrow = 1)## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Plots can say more than me)
Visualize altogether
gg <- ggplot() +
geom_point(data = NMDS_sm,
aes (x = NMDS1, y = NMDS2,
shape = Habitat), alpha = 0.5) +
stat_contour(data = NMDS_os_EW, aes(x = x, y = y, z = z, colour = ..level..)) +
geom_segment(data = NMDS_ef[6:10,],
colour = "blue", size = 0.5, linewidth = 0.25,
aes(x = 0, xend = NMDS1, y = 0, yend = NMDS2, text = label),
arrow = arrow(length = unit(0.25, "cm"))) +
labs(x = "NMDS1", y = "NMDS2") +
theme_minimal() +
scale_color_viridis("Coordinate, western") +
coord_fixed()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning in geom_segment(data = NMDS_ef[6:10, ], colour = "blue", size = 0.5, :
## Ignoring unknown aesthetics: textggplotly(gg)Now we can finally postulate presence of 3 samples groups: western OldLow+OldSlope groups, OldHigh + central and eastern OldLow+OldSlope, and swamp+outliers.
So, the main features, influencing on samples are habitat and their eastern-western position.